100daysofML

I am using a open source platform to showcase my work and motivate others to work along with me.

Day1: 27th Nov

Mobile Price Classification

Day2: 28th Nov

XGBoost Link1 Link2 How to use XGBoost with Python

Day3: 30th Nov

Pytorch Udacity Introduction Lesson 2 (Videos 1 to 25 complete)

Day4: 1st Dec

Study about Accuracy Paradox

Day5: 2nd Dec

Classification Algorithms study: KNN, SVC, K-medoid

Day6: 4th Dec

Studied About Apriori Algorithm Association rule, How is it different from collaborative filtering? Studied about example of market basket analysis.

Apriori Introduction Apriori vs Collaborative filtering

Day7: 5th Dec

Studied about Spectral Clustering

Day8: 6th Dec

Studying Natural Language Processing: CFG, CNF, CYK CYK tells whether a given sentence can be generated from a given Content free grammer given.

Chomsky Normal Form is conversion from CFG to CNF. In CNF we have productions of form. A-> BC or A->epsilon

Day9: 10th Dec

Cluster Algorithm KMeans, Heirarchical Clustering

Day10: 11th Dec

Google Crash Course ML

• Introduction
• Rules of Machine Learning
• Framing

Day11: 12th Dec

Worked on Neural Style transfer Project and Watched PyTorch Udacity (Lecture 2) Working on Ben10 dataset

Day12: 13th Dec

PyTorch Udacity Lecture 2 continue

Day13: 15th Dec

Pytorch Udacity Lecture 2 continue

Day14: 16th Dec

Evaluation metric for Classification

• Jaccard Index: JI = |Intersection| / |Union|

  • JI close to 1 means more similarity
  • JI close to 0 means less similarity

• F1-Score = 2* Precision * Recall/ (Precision + Recall)

  • 1 is Best and 0 is Worst

• Log Loss: Output of Class Label is Probability instead of categorical.

  • Log Loss measures the Performance of a classifier where the predicted output is a probability value between 0 or 1.
  • Log Loss calculated by Log loss equation.
  • Log Loss = (y * log(y predicted)) + (1-y) * (log(1 - (y predicted)))
  • Average Log Loss = -1/n * summation((y * log(y predicted)) + (1-y) * (log(1 - (y predicted))))
  • Lower Log Loss means Best Model and Higher Log Loss value means Poor Model.

  

Day15: 17th Dec

Working on Car Dataset Question.

• Shuffle rows of Dataset
  • np.random.shuffle(DataFrame.values)
• Concat two dataframes
  • df1
  • df2
  • frames = [df1,df2]
  • result = pd.concat(frames,axis=1)
• Rename Columns in Pandas
  • df.rename(columns={'A':'a'},inplace=True)

Day16: 20th Dec

• Worked on ZigWheels dataset
• How qcut works?
  • pd.qcut(dataset, precision=3, labels=['low','med','high'])
• mean_squared_log_error
• average_precision_score works on y_true binary and y_scores continous

Day17: 23rd Dec

Udacity PyTorch Lecture 2, neural network finished.

Day18: 24th Dec

Udacity Talk on PyTorch Lecture 3 finished.

Started Lecture 4, Pytorch

• Single Layer Neural network
  • features = torc.rand((1,5)) # createda (1,5) shape tensor
  • Method 1 :y = activation(torch.sum(features*weights)+bias)
  • Method 2: weights = weights.view(5,1) # used to reshape a tensor vector
    • y = activation(torch.mm(features,weights)+bias)

Day19: 27th Dec

Lecture 4 Started

Day20: 29th Dec

Lecture 4 Continue

Day21: 1st Jan

Lecture 4 Almost finished Learned how to Save Weights of a Trained Model.

Day22: 2nd Jan

Finished Lecture 4 and Started lecture 5 CNN chapter start watched videos till Video 14.

Day23/24/25: 4th Jan - 6th Jan

PyTorch Project on Google Colab Started.

• Google Colab File FlowerClassification
• Model Link

Day26: 10th Jan

R programming Decision Tree, PCA, NaiveBayes, Linear Regression

Day27: 20th Jan

Artcile on Feature Selection

• Link

• Correlation Method

• Techniques

Difference B/W Covariance and Correlation

Feature Selection [ VERY IMPORTANT TOPIC ]:

Why is feature selection Important?

• Training time increases exponentially with number if features.
• Models have increasing risk of overfitting with increasing number of features.

Feature selection Techniques

1. Filter methods
2. Wrapper methods
3. Embedded methods

Filter methods

Filter method considers the relationship b/w features and the target variable to compute the importance of features.

Wrapper methods

Wrapper Methods generate models with a subsets of feature and gauge their model performances.

Embedded Methods

Feature selection by insights provided by some Machine Learning Model.

Day28: 24th Jan

Loss Functions:- Loss Functions

How to determine the value of K in clustering problems?

• 3 Methods: Elbow Method, Average Silhouette method, Gap statistic method
• Resource Link2
• Gap statistic

What causes overfitting, How to prevent it?

• Overfitting causes: Too many features, High epochs training with validation loss, many hidden layers, Good Performance in training set and poor generalization in test set.
• What is it?
• Conceptual Explaination
• Deep learning and overfitting

What is SVD(Singular value decomposition)?

• Mainly used for matrix decomposition
• Mostly used in Recommendation systems.
• SVD link1
• SVD link2
• SVD MIT tutorial
• BEST explaination SVD
• Working and calculation Video

Day30: 17th Sept 2020

• Learning about Ranking Problems with MCDA or MCDM.
• Understanding how MCDM works with Research Paper LINK
• Studied WSM, WPM, AHP, ELECTRE, TOPSIS and MOORA
• Basics of MCDA Youtube Video

Day31: 18th Sept 2020

• Study Comparative Analysis of MCDA techniques LINK1 | LINK2